Discussing Indexing and Embedding Performance in Typesense

Gustavo had issues upgrading their cluster and their embedding field wasn't being filled. Jason helped to solve the upgrade issue and advised re-indexing the documents to solve the embedding field issue. Both problems were successfully resolved.

Samuel experienced issues when enabling embeddings on a large collection, leading to an unhealthy cluster. Kishore Nallan suggested rolling back to a previous snapshot, advised on memory calculations for OpenAI embeddings, and confirmed that creating a new cluster should solve the problem.

Bill sought clarification on using vector search with multiple word embeddings in Typesense and using them instead of OpenAI's embedding. Kishore Nallan and Jason informed him that their development version 0.25 supports open source embedding models. They also resolved Bill's concerns regarding search performance, language support, and limitations in the search parameters.

Erik sought advice for semantic search implementation in Typesense and raised issues around slow document import and excessive latency. Upon implementing advice from Kishore Nallan to try different models, Erik reported faster times, ultimately deciding to rate-limit imports.

Denny inquired about the ideal cluster configuration for handling 1M documents with openAI embedding. Jason recommended a specific configuration, explained record size calculation, and clarified embedding generation speed factors and the conditions that trigger openAI.